Numerous attempts have been engaged in order to lessen the overfitting problem in classification tasks, such as early-stopping training or even introducing regularization methods such as soft-weight sharing [1]}, L1 [2]}, and L2 [3]}, DropConnect [4]}, among others. Alternatively, the best way to employ a regularization method would be to average the predictions of all possible parameter configurations, weighing the possibilities and checking out which would perform better. Nevertheless, such a methodology demands a cumbersome computational effort, only feasible for pitiful or non-complex models [5]}.
